Jing Sui

Full Professor

Institute of Automation, Chinese Academy of Sciences
Telephone: 010-82544518 
Address: 95 Zhongguancun East Road,Haidian district, Beijing, China
Postcode: 100190 

Research Areas

¨       Machine Learning. Classification. Individualized Prediction. 

¨       Biomarker/Neuromarker Identification. Precision Medicine

¨       Brain Imaging Data Analysis (fMRI, dMRI, sMRI). Multimodal Fusion.

¨       Schizophrenia. Depression. Bipolar disorder, and other mental diseases.

¨       Image/Signal Processing. Large Scale Data Mining.  Multivariate Modeling. ICA/CCA.




Optical Engineering (in major of Image/Signal Processing),



Beijing Institute of Technology (BIT), Beijing, China.



Optical Technology and Photoelectric Instrumental, BIT



Computer Science, Beijing Institute of Technology


Work Experience

2013-present    Full Professor, Institute of Automation, CAS, China 

2012-2013        Assistant Professor, The Mind Research Network(MRN),Albuquerque, NM, USA 

2010-2012        Research Scientist, The Mind Research Network , Albuquerque, NM, USA

2007-2009        Postdoctoral Fellow, The Mind Research Network, Albuquerque, NM, USA.

Teaching Experience

2017.3            Teach class “Brain-machine interface”

2015.12          Teaching machine learning class for international CAS graduate students on "Data driven techniques in brain imaging”.

2012. 9           ECE 590 Graduate Seminar on Multimodal Fusion, University of New Mexico(UNM).

2006. 9.          Teaching Image Analysis for graduate students of OE, BIT.

Honors & Distinctions

2015                  Selected Young PI of CAS Center for Excellence in Brain Science and Intelligence Technology

2014                  Best Lecture, in the 11th Annual Conference of Neuroscience, China

2013                  100  Talents Plan Awardee, Chinese Academy of Science

2010                  Outstanding Young Investigators MRN



Selected publications from 60+ peer-reviewed journal papers

1.         Qi S, Yang X, Zhao L, Calhoun VD, Perrone-Bizzozero N, Liu S, Jiang R, Jiang T, Sui J*, Ma X*. 2017. MicroRNA132 associated multimodal neuroimaging patterns in unmedicated major depressive disorder. Brain. In press.

2.         Qi, S., Calhoun, V.D., van Erp, T.G.M., Bustillo, J., Damaraju, E., Turner, J.A., Du, Y., Yang, J., Chen, J., Yu, Q., Mathalon, D.H., Ford, J.M., Voyvodic, J., Mueller, B.A., Belger, A., McEwen, S., Potkin, S.G., Preda, A., Jiang, T., and Sui J.* ‘Multimodal Fusion with Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia’, IEEE Trans Med Imaging, 2017. In press.

3.         Jiang R, Abbott CC, Jiang T, Du Y, Espinoza R, Narr KL, Wade B, Yu Q, Song M, Lin D, Chen J, Jones T, Argyelan M, Petrides G, Sui J* , Calhoun VD (2017): SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets. Neuropsychopharmacology. In press.

4.         He H, Sui J*, Du Y, Yu Q, Lin D, Drevets WC, Savitz JB, Yang J, Victor TA, Calhoun VD. 2017. Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders. Brain Struct Funct. 222:4051-4064.

5.         Meng X, Jiang R, Lin D, Bustillo J, Jones T, Chen J, Yu Q, Du Y, Zhang Y, Jiang T, Sui J*, Calhoun VD. Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data. NeuroImage Jan 15 2017;145(Pt B):218-229.

6.         Sui J., Pearlson, G.D., Du, Y., Yu, Q., Jones, T.R., Chen, J., Jiang, T., Bustillo, J., Calhoun, V.D., 2015. In Search of Multimodal Neuroimaging Biomarkers of Cognitive Deficits in Schizophrenia. Biological Psychiatry. 78(11):794-804. PMCID: PMC4547923.

7.         Abbott CC, Loo D, Sui J. 2016. Determining Electroconvulsive Therapy Response With Machine Learning. JAMA Psychiatry. Editorial. In press

8.         Jie N, Zhu M, Ma X, Osuch EA, Wammes M, Jean T, Li H, Zhang Y, Jiang T, Sui J* , Calhoun, VD. 2016. Discriminating Bipolar Disorder From Major Depression Using Whole Brain Functional Connectivity: A Feature Selection Analysis With SVM-Foba Algorithm. Journal of Singal Processing Systems. In press.

9.         Wade B*, Sui J, Stephanie Njau, Amber Leaver, Megha Vasavada, Boris Gutman, Paul Thompson, Randall Espinoza, Roger Woods, Christopher Abbott, Shantanu Joshi, Katherine Narr. Inter and Intra- hemispheric Structural Imaging Markers Predict Depression Relapse After Electroconvulsive Therapy: a Multisite Study. 2017 Translational Psychiatry. In press.

10.       Lerman-Sinkoff D.B., Sui J., Srinivas R., Kandala S., Calhoun V.D., Barch, D.M. Multimodal neural correlates of cognitive control in the Human Connectome Project. Neuroimage. 2017. In press.

11.       Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. NeuroImage Jan 15 2017;145(Pt B):137-165.

12.       Lin, D., Chen, J., Ehrlich, S., Bustillo, J.R., Perrone-Bizzozero, N., Walton, E., Clark, V.P., Wang, Y.P.,  Sui J., Du, Y., Ho, B.C., Schulz, C.S., Calhoun, V.D., and Liu, J.: ‘Cross-Tissue Exploration of Genetic and Epigenetic Effects on Brain Gray Matter in Schizophrenia’, Schizophr Bull, 2017. In press.

13.       Sui J, Huster R, Yu Q, Judith M. Segall, Vince D Calhoun. 2014. Function-Structure Associations of the Brain: Evidence from Multimodal Connectivity and Covariance Studies. Neuroimage. 102:11-23.

14.       Sui J, He H, Pearlson GD, Adali T, Yu Q, Clark VP, White T, Mueller BA, Ho BC, Andreasen NC, Calhoun VD. 2013. Three-Way (N-way) Fusion of Brain Imaging Data Based on mCCA+jICA and Its Application to Discriminating Schizophrenia. Neuroimage. 2(66):119-132.

15.       Sui J, He, H., Yu, Q., Chen, J., Rogers, J., Pearlson, G.D., Mayer, A., Bustillo, J., Canive, J., Calhoun, V.D., 2013. Combination of Resting State fMRI, DTI, and sMRI Data to Discriminate Schizophrenia by N-way MCCA + jICA. Front Hum Neurosci 7, 235.

16.       Sui J, Adali T, Yu Q, Calhoun VD. 2012. A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data. Journal of Neuroscience Methods. 204(1): 68–81.

17.       Sui J, Yu Q, He H, Pearlson GD, Calhoun VD. 2012. A Selective Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data. Frontiers in Human Neuroscience. 6:27.

18.       Sui J, Pearlson GD, Adali T, Caprihan A, Liu J, Yamamoto J, Calhoun VD. 2011. Discriminating Schizophrenia and Bipolar Disorder by Fusing FMRI and DTI in a CCA+ICA Based Model. Neuroimage. 57(7):839-855.

19.       Sui J, Adali T, Pearlson GD, Yang H, Sponheim SR, White T, Calhoun VD 2010. A CCA+ICA Based Model for Multi-Task Brain Imaging Data Fusion And Its Application to Schizophrenia. Neuroimage. 51(5):123-134.

20.       Sui J, Adali T, Pearlson GD, Calhoun VD. 2009. An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques. Neuroimage 46(1):73- 86.

21.       Sui J Adali T, Pearlson GD, Clark VP, Calhoun VD. 2009. A method for accurate group difference detection by constraining the mixing coefficients in an ICA framework. Hum Brain Mapping 30(9): 2953-2970.


Research Interests

Biomarker/Neuromarker Identification. Precise Medicine

Brain Imaging Data Analysis (fMRI, dMRI, sMRI, EEG). Multimodal Fusion.

Schizophrenia. Depression. Bipolar disorder and other mental Diseases.

Individualized Prediction. Multivariate Modeling. Machine Learning.

Image Processing. Large Scale Data Mining.  ICA/CCA/PCA




Yale University, Duke University, UNC, Harvard University, UNM, UCLA etc.



戚世乐  博士研究生  081104-模式识别与智能系统  


罗娜  博士研究生  081104-模式识别与智能系统  

燕卫政  博士研究生  081104-模式识别与智能系统  

姚东任  博士研究生  081104-模式识别与智能系统  

支冬梅  硕士研究生  081104-模式识别与智能系统  

高爽  博士研究生  081104-模式识别与智能系统  

王乐  博士研究生  081104-模式识别与智能系统  

姜荣涛  博士研究生  081104-模式识别与智能系统  

孙海伦  硕士研究生  081104-模式识别与智能系统